Leong, M. C., Gu, Y., Tan, H. L., Li, L., & Chen, N. (2026). Explicit Logic Channel for Validation and Enhancement of MLLMs on Zero-Shot Tasks. European Conference on Computer Vision (ECCV), 2026.
Abstract:
Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are often deployed as zero-shot solution to new tasks in a black-box manner. Validating and understanding the behavior of these models become important for application to new task. We propose an Explicit Logic Channel, in parallel with the black-box model channel, to perform explicit logical reasoning for model validation, selection and enhancement. The frontier MLLM, encapsulating latent vision-language knowledge, can be considered as an Implicit Logic Channel. The proposed Explicit Logic Channel, mimicking human logical reasoning, incorporates a LLM, a VFM, and logical reasoning with probabilistic inference for factual, counterfactual, and relational reasoning over the explicit visual evidence. A Consistency Rate (CR) is proposed for cross-channel validation and model selection, even without ground-truth annotations. Additionally, cross-channel integration further improves performance in zero-shot tasks over MLLMs, grounded with explicit visual evidence to enhance trustworthiness. Comprehensive experiments conducted for two representative VLC tasks, i.e., MC-VQA and HC-REC, on three challenging benchmarks, with 11 recent open-source MLLMs from 4 frontier families. Our systematic evaluations demonstrate the effectiveness of proposed ELC and CR for model validation, selection and improvement on MLLMs with enhanced explainability and trustworthiness.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - National Large Language Models Funding Initiative (AISG Award No: AISG-NMLP-2024-004)
Grant Reference no. : AISG-NMLP-2024-004
Description:
This is a post-peer-review, pre-copyedit version of an article published in ECCV 2026 Proceedings. The final authenticated version is available online at: https://link.springer.com/book/9783032369833